Are consumers willing to pay for letting the car drive for them? - PowerPoint PPT Presentation

Are consumers willing to pay for letting the car drive for them? Analyzing response to autonomous navigation Ricardo A Daziano 1 & Benjamin Leard 2 1 School of Civil and Environmental Engineering, Cornell University; 2 Resources for the Future January 2015

Motivation Veh choice & WTP inference Empirical application Conclusions Technological change in the automotive industry 1 Powertrain: re-emergence of electric vehicles (BEVs), commercialization of PHEVs 2 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Technological change in the automotive industry 1 Powertrain: re-emergence of electric vehicles (BEVs), commercialization of PHEVs 2 Automated vehicles 2 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Personal transportation - energy conversion Internal combustion engines are highly inefficient 3 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Personal transportation - energy conversion Internal combustion engines are highly inefficient Tank-to-wheel energy efficiency is about 15% 3 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Personal transportation - energy conversion Internal combustion engines are highly inefficient Tank-to-wheel energy efficiency is about 15% Engine loss is 76% 3 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Personal transportation - energy conversion Internal combustion engines are highly inefficient Tank-to-wheel energy efficiency is about 15% Engine loss is 76% About 1% of the energy is used to transport the driver 3 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Battery Electric Vehicles Battery electric vehicles (BEVs) are propelled by one or more electric motors that are powered by rechargeable EV batteries BEVs tank-to-wheel efficiency: ∼ 85% Electrification : pertinent step toward energy sustainability in personal transportation BEVs have the potential for being charged using clean energy sources (cf. Zivin et al., NBER 2012) 4 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions BEV adoption BEVs were (re)introduced into the US market in 2011 Li-ion batteries (most charge capacity, but high cost per kWh of storage) Emerging market with slow consumer shift, despite important operating cost savings (cost equivalent to $1/gal) 2014 PEV sales: rose above the 100,000 level 2014 Nissan LEAF: 30,200 deliveries (22,610; < 10K) 5 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Low emission vehicles and range anxiety Range anxiety: important barrier to BEV adoption For planning a successful introduction of LEVs in the market it becomes essential to fully understand consumer valuation of driving range Why is driving range limited? 1 Production cost of batteries is a function of range 2 Added weight is needed to extend range 6 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions From the LEAF Facebook page 7 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Automated vehicles: at least some control functions occur without direct input from the driver 1 Autonomous : use vehicle sensors only 2 Connected : V2V communication 8 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions The Transformative Nature of Automation Transportation Systems Revolution Safety (crash avoidance) 1 Efficiency (reduced congestion; energy and env benefits) 2 Accessibility (improved mobility) 3 9 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Intelligent Driving 10 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Autonomous parking 11 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions What about full automation? Google car: 700,000+ miles driven Tesla Model S autopilot features (incremental automation) 12 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Choice Modeling: consumer response Microeconometric discrete choice models of demand (McFadden, AER 2001) Probabilistic models of economic choice among a finite group of differentiated products Quantitative understanding of the tradeoffs across product characteristics Indirect mechanism to determine willingness to pay Widely used in Applied economics (health & labor, environment) 1 Marketing 2 Political science (voting preferences) 3 Urban planning 4 Some fields of civil engineering (transportation analysis) 5 13 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Transportation analysis Researchers, firms, and policy-makers use discrete choice models to predict demand for new alternatives and infrastructure (e.g. a light 1 rail or a new highway) inform traffic assignment models (route choice) 2 analyze the market impact of firm decisions (e.g. merger of two airline 3 companies) set pricing strategies (e.g. congestion pricing, revenue management) 4 prioritize research and development decisions (e.g. automotive 5 industry ) perform cost-benefit analyses of transportation projects (e.g. new 6 bridge or tunnel) understand car ownership ( vehicle choice ) 7 14 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Random utility maximization Theory of individual choice behavior: individuals make choices by maximizing their satisfaction Operational model: satisfaction is measured using U ij = v ij ( q ij , I i − p ij , ε ij | θ ) v ij indirect utility of alternative j for individual i q ij vector of attributes that characterize the alternatives I i income p ij price of the alternative ε ij taste shocks (unobserved heterogeneity) θ unknown preference parameters Chosen alternative i j is such that U i j j = max U ij j Regression with LDV 15 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Modeling adoption of high technology, durable goods Barriers to adoption and diffusion WTP for new technology affected by attitudes, knowledge, and social network effects Asymmetric investment with associated uncertainties and subjective risks Energy-efficient technology: willingness to pay for fuel savings (Greene and Hiestand, 2013) 16 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Vehicle choice model General model U ij = − α price ij + β PVFC PVFC ij + β ln range , i ln( range ij )+ x ′ ij β i + ε ij L ij E ( fc ijt ) � PVFC ij = (1 + r ) t t =1 1 Endogenous discounting (Hausman, 1979; Greene, 1983; Train, 1985) 2 Exogenous discounting (Allcott and Wozny, 2012) 17 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Endogenous discounting (Hausman, RAND 1979) r is treated as an additional parameter If L is large enough and appreciation in fuel prices is ignored, then the capitalized worth approximation can be used: PVFC in ≈ fc ij , r where fc in is a uniform equivalent of E ( fc ijt ) β fc = β PVFC / r For a rational consumer ( − α = β PVFC ), then r = − α 1 = , β fc WTP fc where WTP fc is the willingness to pay for marginal savings in fuel cost. 18 of 38 School of Civil and Environmental Engineering

Motivation Veh choice & WTP inference Empirical application Conclusions Exogenous discounting (Allcott & Wozny, REST 2012) Market failures may explain myopic discounting in the sense that − α � = β PVFC DCM in WTP-space (A&W, 2012; Newell & Siikam¨ aki, NBER 2013) : � � price ij + γ PVFC PVFC ij − x ′ U ij = − α ij ω x + ε ij , where γ PVFC is the willingness to pay for marginal savings in the present value of lifecycle costs If γ PVFC < 1, then there is evidence for myopic consumption 19 of 38 School of Civil and Environmental Engineering